Transition to Sustainable Smart Energy: An analysis of human-building interactions in Qatar Using Machine Learning Techniques.

Human-oriented factors present unavoidable challenges and uncertainties in building energy strategic planning. The uncertainties escalate when the target society is not fully known to the decision-maker and can create performance gaps between the expected and actual outcomes of sustainability targets. This article aims to investigate the role of socioeconomic and behavioral dimensions in residential energy consumption patterns among regions that host high proportions of migrant communities with diverse cultural and ethnic traits. This study evaluates the patterns in human-building interactions and energy behaviors among local and migrant communities based on empirical evidence and survey analysis. The survey data are investigated via machine learning approaches to identify the interdependencies between and feature importance of critical factors that influence human-building interactions and to determine elements that help to discern the energy behavior of locals and migrants. A simulation analysis is conducted to analyze residential energy consumption under different human indoor thermal comfort preferences in multiple case scenarios to demonstrate how improvements in human-building interaction can create saving opportunities. The findings capture the main socioeconomic and behavioral contributors in residential energy consumption and demonstrate the impact of human factor at a high level in regions with imbalanced demographics and societies in transition.